A model-based ranking of U.S. recessions
Ivan Jeliazkov () and
Rui Liu ()
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Rui Liu: UC Irvine
Economics Bulletin, 2010, vol. 30, issue 3, 2289-2296
A dynamic factor VAR model, estimated by MCMC simulation, is employed to assess the relative severity of post-war U.S. recessions. Joint modeling and estimation of all model unknowns yields rank estimates that fully account for parameter uncertainty. A convenient by-product of the simulation approach is a probability distribution of possible recession ranks that (i) accommodates uncertainty about the exact location of troughs, and (ii) can be used to resolve any potential inconsistencies or ties in the rank estimates. These features distinguish the approach from single-variable measures of downturns that ignore the co-movement and dynamic dependence and could lead to contradictory conclusions about timing and relative severity.
Keywords: Bayesian estimation, business cycle, dynamic factor, Markov chain Monte Carlo (MCMC); vector autoregressive (VAR) model (search for similar items in EconPapers)
JEL-codes: C1 E3 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ebl:ecbull:eb-10-00483
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